Ship behavior prediction and anomaly detection using LSTM-DCross model based on AIS and remote sensing data by Wanrong Wu, Dongmei Yan, Jun Yan, Xiaowei Wang

“…This paper proposes a deep cross LSTM network (LSTM-DCross) based on AIS time series data to overcome limitations in traditional approaches. The model integrates an encoder-decoder framework with an attention mechanism to predict ship behaviours, including position (longitude, latitude), course (COG), and speed (SOG). …”
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Developing a Prediction Model for Real-Time Incident Detection Leveraging User-Oriented Participatory Sensing Data by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

“…Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. …”
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Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods by Hassan Hassani, Esfandiar Malekian Kallehbasti, Yahya Kamyabi

“…The results show that the performance of accrual-based earnings management forecasting methods based on the relief-based feature selection model is better than the feature selection model based on principal component analysis. …”
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AI-driven epidemic intelligence: the future of outbreak detection and response by Jasleen Kaur, Jasleen Kaur, Zahid Ahmad Butt

“…Epidemic intelligence, the process of detecting, verifying, and analyzing public health threats to enable timely responses, traditionally relies heavily on manual reporting and structured data, often causing delays and coverage gaps. …”
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Precise irrigation of dryland cotton under canal irrigation system constraints based on the CERES-CROPGRO-Cotton model by Lei Wang, Liang He, Weihong Sun, Chen Gao, Zhenxiang Han, Meiwei Lin

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Optimizing maize germination forecasts with random forest and data fusion techniques by Lili Wu, Yuqing Xing, Kaiwen Yang, Wenqiang Li, Guangyue Ren, Debang Zhang, Huiping Fan

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Frost forecasting via weakly-supervised semantic segmentation of satellite imagery by Seokho Kang, Seon-Kyeong Seong, Eunha Sohn, Jiyoung Kim, Jaewoong Shim

“…This can be formulated as a semantic segmentation task of detecting areas where frost is likely to occur. Using satellite images and geographical information of the target region at the forecast time as inputs, the semantic segmentation model generates a frost probability map for the target time of forecast. …”
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Assessment of the possibility of forecasting hydrometeorological extremes using the solar activity index by Miskevich I.V., Kotova E.I.

“…The method can be used for specifying longterm hydrometeorological and ecological forecasts. This approach can be used as an alternative way of detecting geoecological anomalies.…”
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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

“…A stacking ensemble model is developed with support vector machine (SVM), K-nearest neighbors (KNN), and extreme learning machine (ELM) to enhance forecast accuracy. …”
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Optimizing Smart Grid Load Forecasting via a Hybrid Long Short-Term Memory-XGBoost Framework: Enhancing Accuracy, Robustness, and Energy Management by Falah Dakheel, Mesut Çevik

“…An experimental attempt to integrate attention mechanisms was also conducted, but it did not enhance the performance and was therefore excluded from the final model. The results extend the literature on the development of fusion-based machine learning models for time series forecasting, and the future work of energy consumption analysis, anomaly detection, and resource allocation in SM grids.…”
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Forecasting Foodborne Disease Risk Caused by <i>Vibrio parahaemolyticus</i> Using a SARIMAX Model Incorporating Sea Surface Environmental and Climate Factors: Implications for Seaf... by Rong Ma, Ting Liu, Lei Fang, Jiang Chen, Shenjun Yao, Hui Lei, Yu Song

“…The SARIMAX model, which incorporates both marine and climatic factors, was developed to facilitate short-term forecasts of <i>V. parahaemolyticus</i> detection rates in coastal cities. …”
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Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines by Cheng Yin, Le Thanh Binh, Duong Tran Anh, Son T. Mai, Anh Le, Van-Hau Nguyen, Van-Chien Nguyen, Nguyen Xuan Tinh, Hitoshi Tanaka, Nguyen Trung Viet, Long D. Nguyen, Trung Q. Duong

“…The forecasting performance of the SARIMA model, NNAR model and LSTM model is comparable in both long and short-term predictions. …”
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A Unified Transformer–BDI Architecture for Financial Fraud Detection: Distributed Knowledge Transfer Across Diverse Datasets by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

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A Trust Based Anomaly Detection Scheme Using a Hybrid Deep Learning Model for IoT Routing Attacks Mitigation by Khatereh Ahmadi, Reza Javidan

“…We have formulated the problem of routing behavior anomaly detection as a time series forecasting method, which is solved based on a stacked long–short term memory (LSTM) sequence to sequence autoencoder; that is, a hybrid training model of recurrent neural networks and autoencoders. …”
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Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images by Cristian Crisosto

“…A persistence model forecast to provide a reference for comparison was generated. …”
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Forecasting the number of identified information security vulnerabilities based on the theory of “Gray Systems” by A. O. Efimov, S. A. Mishin, E. A. Rogozin

“…The use of a linear regression model and models based on the theory of “gray systems” to predict the number of identified vulnerabilities allows you to get close forecast values. …”
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Addressing the training gaps for ASD specialists in Kazakhstan: A forecast-based approach by Ainagul Rymkhanova, Saltanat Mulikova, Alfina Sakayeva, Zhanar Akhmetzhanova, Sara Zhanibekova, Assem Bekmaganbetova

“…Additionally, mathematical modeling and forecasting methods were employed to predict the increase in the number of children with ASD, facilitating the development of targeted educational programs for training specialists at undergraduate and graduate levels and improving the qualifications of practicing professionals. …”
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Development of Smart Models to Accurately Predict Dynamic Viscosity of CO2-Saturated Polyethylene Glycol by Ayat Hussein Adhab, Morug Salih Mahdi, Bhavesh Kanabar, Anupam Yadav, Ranganathaswamy M K, Rishabh Thakur, Parveen Kumar, Braj Krishna, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

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ENERGY DEMAND FORECAST FOR TURKISH AGRICULTURE SECTOR: GRANGER CAUSALITY AND COINTEGRATION TEST by Mehmet Arif ŞAHİNLİ, Ahmet ÖZÇELİK

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Forecasting the Fast Radio Burst Population Observed through Galaxy Cluster Lenses by Mawson W. Sammons, Evan Davies-Velie, Matt Dobbs, Zarif Kader, Seth R. Siegel, Jonathan Sievers

“…In many other fields, gravitational lensing from galaxy clusters has enabled high-redshift detections by magnifying background sources. In this work we forecast the populations of FRBs expected to be detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and upcoming instrument Canadian Hydrogen Observatory and Radio Transient Detector (CHORD), for blank fields and by lensing through a range of strong lensing galaxy clusters, based on existing, observationally driven cluster models. …”
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